Chapter
Aug 12, 2020
Long Short-Term Memory Neural Network for Travel Time Prediction of Expressways Using Toll Station Data
Authors: Deqi Chen [email protected], Xuedong Yan [email protected], Shurong Li [email protected], Liwei Wang [email protected], and Xiaobing Liu [email protected]Author Affiliations
Publication: CICTP 2020
ABSTRACT
Based on deep learning methods, especially long short-term memory (LSTM) neural networks, short-term traffic forecasting has achieved explosive growth. This study proposes the Bi-LSTM model to effectively predict travel time. In order to validate the effectiveness of the proposed stacked LSTM, we used 9-day toll station entry and exit data from the expressways of Guangdong province with an updating frequency of 5 min. The experimental result indicates that excessive depths of the model will lead to the increase of loss values. Moreover, the stability of data will affect the prediction accuracy. In addition, compared with other machine learning methods, as well as different topologies of neural networks, the stacked Bi-LSTM neural network has advantages of reliability, accuracy, and stability, which could facilitate travel time prediction.
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CICTP 2020
Pages: 73 - 85
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Aug 12, 2020
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1MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, PR China. Email: [email protected]
2MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, PR China. Email: [email protected]
3MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, PR China. Email: [email protected]
4MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, PR China. Email: [email protected]
5MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, PR China. Email: [email protected]
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